Evidence map›Paper›PMID 37278690›Full record

Trial reportJournal of clinical and experimental neuropsychology2023

Neurocognitive predictors of adherence to an online pain self-management program adjunct to long-term opioid therapy.

David E Fleck, Marian Wilson, Daniel Lewis, Jeffrey A Welge, Grace Arya, Anoop Sathyan, Kelly Cohen, T John Winhusen

Registry-linked trialAbstract readRandomized Controlled Trial
PubMed Full text at PMC Publisher
In one paragraph

Trial report in Journal of clinical and experimental neuropsychology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT03309188, which is not on this map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT03309188 no registry record found
3 · Its place in the literature

Who cites it

4 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Moving towards the use of artificial intelligence in pain management.European journal of pain (London, England) · 2025
    Pooled it
  2. Review
  3. Review
  4. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

David E FleckDepartment of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, OH, USA.
Marian WilsonCollege of Nursing, Washington State University, Spokane, WA, USA.
Daniel LewisDepartment of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, OH, USA.
Jeffrey A WelgeDepartment of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, OH, USA.
Grace AryaDepartment of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, OH, USA.
Anoop SathyanDepartment of Aerospace Engineering and Engineering Mechanics, University of Cincinnati College of Engineering and Applied Science, Cincinnati, OH, USA.
Kelly CohenDepartment of Aerospace Engineering and Engineering Mechanics, University of Cincinnati College of Engineering and Applied Science, Cincinnati, OH, USA.
T John WinhusenDepartment of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, OH, USA.

Funding

EMPOWER: Evaluating the ability to reduce Morphine equivalent dose for chronic Pain patients receiving Opioid-therapy through a Web-based E-Health self-management program: a Randomized multi-site ClinR01DA044248 · NIDA · UNIVERSITY OF CINCINNATI · PI WINHUSEN, T JOHN · 2017 to 2021
$2.5M
NIDA NIH HHS R01 DA044248
6 · The paper itself

Abstract

introductionWhile pain self-management programs can significantly improve patient outcomes, poor adherence is common and the need for research on predictors of adherence has been noted. A potential, but commonly overlooked, predictor is cognitive function. Our aim, then, was to examine the relative influence of various cognitive functional domains on engagement with an online pain self-management program.

methodA secondary analysis of a randomized controlled trial testing the impact of E-health (a 4-month subscription to the online Goalistics Chronic Pain Management Program) plus treatment as usual, relative to treatment as usual alone, on pain and opioid dose outcomes in adults receiving long-term opioid therapy of morphine equivalence dose ≥20 mg; 165 E-health participants who completed an on-line neurocognitive battery were included in this sub-analysis. A variety of demographic, clinical, and symptom rating scales were also examined. We hypothesized that better processing speed and executive functions at baseline would predict engagement with the 4-month E-health subscription.

resultsTen functional cognitive domains were identified using exploratory factor analysis and the resultant factor scores applied for hypothesis testing. The strongest predictors of E-health engagement were selective attention, and response inhibition and speed domains. An explainable machine learning algorithm improved classification accuracy, sensitivity, and specificity.

conclusionsThe results suggest that cognition, especially selective attention, inhibitory control, and processing speed, is predictive of online chronic pain self-management program engagement. Future research to replicate and extend these findings seems warranted. CLINICALTRIALS.GOV REGISTRATION NUMBER: NCT03309188.

Indexed as

Chronic PainSelf-ManagementAdultAnalgesics, OpioidHumansPain ManagementAnalgesics, OpioidChronic Pain Management Programcognitionneuropsychologyopioid therapyPain

Identifiers

PMID37278690
PMCPMC10526690

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Registered trials

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.